Ship great agents fast with our open source JS frameworks – LangChain, LangGraph, and Deep Agents. Maintained by @LangChain.
Joined January 2026
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@𝚕𝚊𝚗𝚐𝚌𝚑𝚊𝚒𝚗/𝚖𝚌𝚙-𝚊𝚍𝚊𝚙𝚝𝚎𝚛𝚜 2.0 is out! 🧵
Using MCP with your TypeScript agents up to MCP servers just got a lot simpler: support for the latest stateless version of MCP, tools that can check in with your users when they need something, much easier auth integrations, and a ton of QoL updates.
Here's what's new 👇
Plus a bunch of QOL cleanup to make the adapter easier to build with:
— MultiServerMCPClient → MCPAdapter, mcpServers → servers, getTools() → listTools() (old names still work)
— Tool names carry the server name, so two servers can both expose search
— Tool errors from the server reach the model as error ToolMessages
Go try it and let us know what you think! 𝚙𝚗𝚙𝚖 𝚒 @𝚕𝚊𝚗𝚐𝚌𝚑𝚊𝚒𝚗/𝚖𝚌𝚙-𝚊𝚍𝚊𝚙𝚝𝚎𝚛𝚜
New to MCP in LangChain? Start here: docs.langchain.com/oss/javas…
Coming from 1.x? Migration guide: docs.langchain.com/oss/javas…
See the release notes: github.com/langchain-ai/lang…
🧑🍳
Starting today, @LangChain’s Managed Deep Agents can bake your agent’s environment at deploy time. 🧑💻
No clone. No install. No repeated setup.
Drop in setup.sh or a Dockerfile. Bake once. Every new thread starts ready. 👇
You deploy, we provision! 🤝
Starting today, a @LangChain's Managed Deep Agent deploy provisions your Slack app for you 🤯
No manifest. No OAuth redirects. No bot tokens to copy around.
One command, and your agent says 👋 in Slack.
LangChain JS retweeted
An agent is 3️⃣ layers.
👉 Business logic you write.
👉 A harness that runs the loop.
👉 Infrastructure that survives production.
@hwchase17 breaks the whole stack in one video.
youtu.be/TUJmfeGTr1Q
LangChain JS retweeted
Managed Deep Agents is just getting started 🚀
@hwchase17 just published a set for banger videos that get you up to speed how shipping production agents today looks like.
🎓New YouTube playlist: Managed Deep Agents
Gives an overview of Managed Deep Agents, and then each video dives deep into core concepts.
Launching with six videos!
1⃣ Intro: youtu.be/xdrB53bgpp0
2⃣ Conceptual Overview: youtu.be/TUJmfeGTr1Q
3⃣ Quickstart: youtu.be/L54dR9qMKzc
4⃣ Instructions and Context Hub: youtu.be/8HIoZV7qlwA
5⃣ Skills: youtu.be/Lhru0yMI2as
6⃣ Tools: youtu.be/piBwvWgHqkY
Playlist link: youtube.com/playlist?list=PL…
LangChain JS retweeted
The AI engineer's job: what it knows, what it can call, and where people reach it. 🤔🗣️🤝
The rest is Managed Deep Agents.
langch.in/managed-deep-agent…
LangChain JS retweeted
In Managed Deep Agents skills load in two steps, which is why a lean SKILL.md works.
The prompt keeps the name and description. The body opens when the task matches.
docs.langchain.com/langsmith…
Agent skills fail when their instructions are vague and their context is bloated.
Best Practices for Creating Agent Skills is a practical guide for builders writing skills that agents can discover, load, and execute reliably.
It helps you structure skills around lean SKILL.md instructions, just-in-time references, deterministic scripts, and LLM-assisted validation instead of piling everything into one prompt.
Key features:
• Standard skill structure – separates core instructions, scripts, references, and reusable assets
• Discoverable metadata – explains naming rules and trigger-focused descriptions, including negative triggers
• Progressive disclosure – keeps SKILL.md under 500 lines and loads supporting context only when needed
• Procedural guidance – favors numbered steps, concrete templates, consistent terminology, and third-person imperative
• Validation workflow – covers discovery tests, logic simulation, edge-case testing, and architecture refinement
Free public GitHub repo.
Link in the reply 👇
LangChain JS retweeted
Mention the agent in Slack. It runs. It replies in the thread.
Managed Deep Agents treats Slack as a channel file: channels/slack.py. Mentions, DMs, and follow-ups start a run as the caller and post the answer back. No bot server to stand up either.
docs.langchain.com/langsmith…
LangChain JS retweeted
You don't stand up an agent. You upload a folder.
Claude Code is a harness for your laptop. Managed Deep Agents is a harness for production.
Want Slack? Add a file. Want a daily run? Add a file. Want memory? Add a file.
LangSmith runs the harness.
docs.langchain.com/langsmith…
LangChain JS retweeted
We’re launching Managed Deep Agents today 🎉👀
langchain.com/blog/managed-d… @LangChain
The goal posts for building agents have moved.
Giving an agent tools, deploying it somewhere, and putting a UI in front of it isn’t the hard part anymore.
The next challenge is everything around the agent: identity, memory, credentials, permissions, and securely connecting it to the services your users already use.
This beta release is just the beginning and our foundation what is about to come next 🚀
LangChain JS retweeted
this blog covers 6 common workflow patterns we see with dynamic subagents (fanout and synthesize, adversarial verification, etc)
if you'd prefer to learn w/ a video, check out this excellent guide from @colifran_ with examples and trace walk throughs!
code reviews can be beautiful ❤️ thanks @shadcn!
Plugged the new @shadcn chat components into @LangChain_JS Deep Agents and one-shotted a full code review agent.
One reviewer per file, local sandbox, streaming live. Wild how fast this was 😍
🧑💻 full demo: github.com/langchain-ai/stre…
LangChain JS retweeted
Alex recently joined the @LangChain_OSS team, and he published his first article on how Deep Agents uses prompt caching to cut API costs.
Give it a read, and give @its_ao a follow!
